Manager II, Machine Learning - Content Success
Pinterest helps Pinners discover and do what they love. The Content Success team is responsible for ensuring that Pinners see fresh new products, ideas and inspiration in their recommendations and that Content Producers receive value from the platform through exposure, engagement and monetization. We work across different recommendation surfaces (Homefeed, Search, Related Pins, Ads) to develop new integrations and signals. We are looking for an experienced Engineering Manager who can drive the team’s technical direction, lead the team in developing new integrations and signals for recommendation systems and make an impact on Pinterest’s topline metrics.
What you'll do:
Lead, mentor and grow a team of experienced backend and machine learning engineers in developing advanced signals, integrations and systems, which are integral to key Pinterest products across Discovery, Ads, and Growth.
Provide thought leadership in content distribution and recommender systems by setting a long-term technical vision and advancing the state-of-the-art in the field. Act as the glue between content acquisition and recommendation pods becoming the expert in both these areas.
Manage project execution and stakeholder communication, including roadmap planning, technical decision-making, risk mitigation, and progress updates to achieve business goals.
Help the team solve difficult technical challenges such as:
How to build new ML systems, candidate generators, features and models that can handle millions of new Pins every day with low latency to effectively distribute fresh content and be re-usable across recommendation surfaces?
How to determine what fresh content our Pinners will be inspired by and predict their future engagement metrics?
How to identify and remove biases from existing recommendation systems?
How to experiment with and measure the impact of content acquisition changes on the user experience?
How to enhance the titles of new content to increase their engagement?
What we’re looking for:
7+ years of industry experience, including 2+ years of management experience.
Experience with:
developing and deploying large-scale machine learning systems in search and recommendations.
big data technologies (e.g. Hadoop, Spark) and scalable realtime systems that process stream data (e.g Kafka, Flink).
applying NLP models and LLMs to content understanding and recommendation systems.
Experience building and leading high performing teams within a visible business vertical .
Experience working with numerous cross functional partners to drive a collective initiative.
Bachelors degree in a technical field, or equivalent work experience.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
In-Office Requirement Statement:
We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.
#LI-REMOTE
#LI-DM57
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Manager II, Machine Learning - Content Success
Pinterest helps Pinners discover and do what they love. The Content Success team is responsible for ensuring that Pinners see fresh new products, ideas and inspiration in their recommendations and that Content Producers receive value from the platform through exposure, engagement and monetization. We work across different recommendation surfaces (Homefeed, Search, Related Pins, Ads) to develop new integrations and signals. We are looking for an experienced Engineering Manager who can drive the team’s technical direction, lead the team in developing new integrations and signals for recommendation systems and make an impact on Pinterest’s topline metrics.
What you'll do:
Lead, mentor and grow a team of experienced backend and machine learning engineers in developing advanced signals, integrations and systems, which are integral to key Pinterest products across Discovery, Ads, and Growth.
Provide thought leadership in content distribution and recommender systems by setting a long-term technical vision and advancing the state-of-the-art in the field. Act as the glue between content acquisition and recommendation pods becoming the expert in both these areas.
Manage project execution and stakeholder communication, including roadmap planning, technical decision-making, risk mitigation, and progress updates to achieve business goals.
Help the team solve difficult technical challenges such as:
How to build new ML systems, candidate generators, features and models that can handle millions of new Pins every day with low latency to effectively distribute fresh content and be re-usable across recommendation surfaces?
How to determine what fresh content our Pinners will be inspired by and predict their future engagement metrics?
How to identify and remove biases from existing recommendation systems?
How to experiment with and measure the impact of content acquisition changes on the user experience?
How to enhance the titles of new content to increase their engagement?
What we’re looking for:
7+ years of industry experience, including 2+ years of management experience.
Experience with:
developing and deploying large-scale machine learning systems in search and recommendations.
big data technologies (e.g. Hadoop, Spark) and scalable realtime systems that process stream data (e.g Kafka, Flink).
applying NLP models and LLMs to content understanding and recommendation systems.
Experience building and leading high performing teams within a visible business vertical .
Experience working with numerous cross functional partners to drive a collective initiative.
Bachelors degree in a technical field, or equivalent work experience.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
In-Office Requirement Statement:
We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.
#LI-REMOTE
#LI-DM57
